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Multispectral images of color-thermal pairs have shown more effective than a single color channel for pedestrian detection, especially under challenging illumination conditions. However, there is still a lack of studies on how to fuse the two modalities effectively. In this paper, we deeply compare six different convolutional network fusion architectures and analyse their adaptations, enabling a vanilla architecture to obtain detection performances comparable to the state-of-the-art results. Further, we discover that pedestrian detection confidences from color or thermal images are correlated with illumination conditions. With this in mind, we propose an Illumination-aware Faster R-CNN (IAF R-CNN). Specifically, an Illumination-aware Network is introduced to give an illumination measure of the input image. Then we adaptively merge color and thermal sub-networks via a gate function defined over the illumination value. The experimental results on KAIST Multispectral Pedestrian Benchmark validate the effectiveness of the proposed IAF R-CNN.

We present fast algorithms to perform accurate CCD queries between triangulated models. Our formulation uses properties of the Bernstein basis and Bézier curves and reduces the problem to evaluating signs of polynomials. We present a geometrically exact CCD algorithm based on the exact geometric computation paradigm to perform reliable Boolean collision queries. Our algorithm is more than an order of magnitude faster than prior exact algorithms. We evaluate its performance for cloth and FEM simulations on CPUs and GPUs, and highlight the benefits.

Figure 1: Benchmark Andy: Our GPU-based approach can simulate the clothes dressed on a Kung-Fu boy. The meshes of three cloth pieces are represented by 127K triangles. Our simulator performs all of the computations, including implicit time integration and collision handling, in 2.42s per frame (on average) on an NVIDIA Telsa K40c GPU. Our new parallel algorithms for sparse matrix assembly and collision handling result in significant speedups over prior methods. AbstractWe present a novel GPU-based approach to robustly and efficiently simulate high-resolution and complexly layered cloth.The key component of our formulation is a parallelized matrix assembly algorithm that can quickly build a large and sparse matrix in a compressed format and accurately solve linear systems on GPUs. We also present a fast and integrated solution for parallel collision handling, including collision detection and response computations, which utilizes spatio-temporal coherence. We combine these algorithms as part of a new cloth simulation pipeline that incorporates contact forces into implicit time integration for collision avoidance. The entire pipeline is implemented on GPUs, and we evaluate its performance on complex benchmarks consisting of 100 − 300K triangles. In practice, our system takes a few seconds to simulate one frame of a complex cloth scene, which represents significant speedups over prior CPU and GPU-based cloth simulation systems. † {tang_m, tangle,

We present a novel culling algorithm that uses deforming nonpenetration filters to improve the performance of continuous collision detection (CCD) algorithms. The underlying idea is to use a simple and effective filter that reduces both the number of false positives and the elementary tests between the primitives. This filter is derived from the coplanarity condition and can be easily combined with other methods used to accelerate CCD. We have implemented the algorithm and tested its performance on many non-rigid simulations. In practice, we can reduce the number of false positives significantly and improve the overall performance of CCD algorithms by 1.5 − 8.2x.

Our experiments confirm that our method is robust and that the SPC layer helps increase the prediction accuracy. Additionally, the proposed method can easily be extended to other 3D object detection tasks in medical image processing.

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